Abstract
Purpose
The assessment of intra-operatively acquired volumetric data is a difficult and often time-consuming task, which demands a new set of skills from the surgeons. In the case of orthopedic surgeries such as the treatment of calcaneal fractures, the correctness of the reduction of the bone fragments can be verified with the help of C-arm CT volumetric images. For an accurate intra-operative assessment of the displaced fragments, an automatic segmentation of the articular surfaces and color-coded visualization was developed.
Methods
Our automatic approach consists of three major steps: first, using adjusted standard planes intersecting the articular region, the joint space is localized with an intensity profile-based method. In a second step, the localized joint space is segmented on the Laplacian of Gaussian filtered volumetric image by a modified binary flood fill algorithm. Finally, a 3D surface model of the segmented joint space is analyzed and visualized with focus on critical displacements of the surface.
Results
A specifically designed human cadaver study consisting of ten lower legs of ten different donors was conducted to acquire 48 realistic C-arm CT images of misaligned bone fragments (steps of varying sizes) in the posterior talar articular surface of the calcaneus. The proposed algorithmic pipeline was verified by the acquired image data and showed very good results with no false positives and an overall correct displacement assessment of \(93.8\,\%\).
Conclusions
The proposed algorithmic pipeline can be easily integrated into the clinical workflow and qualifies for intra-operative usage. It showed very good results on the reference data set of the cadaver study. With the help of such an assistance system, the time-consuming process of 2D view adjustment and visual assessment of the gray value images can be greatly simplified.
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References
Akhtar S, Poh C, Kitney R (2007) An MRI derived articular cartilage visualization framework. Osteoarthr Cartil 15(9):1070–1085
Bahri N, Simon L, Gaida S, Schulz A, Fuchs S (2010) Impact of intraoperative 3d fluoroscopy scan in the surgical treatment of calcaneal fractures. J Bone Jt Surg Br Vol 92(SUPP II):349–349
Brehler M, Görres J, Wolf I, Franke J, von Recum J, Grützner PA, Meinzer HP, Nabers D (2014) Automatic standard plane adjustment on mobile c-arm ct images of the calcaneus using atlas-based feature registration. In: Proc. SPIE 9036, medical imaging 2014, image-guided procedures, robotic interventions, and modeling, 90360E. doi:10.1109/CVPR.2001.990517
Cao Q, Thawait G, Gang GJ, Zbijewski W, Reigel T, Brown T, Corner B, Demehri S, Siewerdsen JH (2015) Characterization of 3d joint space morphology using an electrostatic model (with application to osteoarthritis). Phys Med Biol 60(3):947
Choi JH, McWalter EJ, Pal S, Maier A, Gold GE, Fahrig R (2013) Analysis of three-dimensional joint space of the tibiofemoral joint. In: Nuclear science symposium and medical imaging conference (NSS/MIC), 2013 IEEE, pp 1–4. IEEE
Daly M, Siewerdsen J, Moseley D, Jaffray D, Irish J (2006) Intraoperative cone-beam ct for guidance of head and neck surgery: assessment of dose and image quality using a c-arm prototype. Med Phys 33(10):3767–3780
Franke J, Wendl K, Suda AJ, Giese T, Grützner PA, von Recum J (2014) Intraoperative three-dimensional imaging in the treatment of calcaneal fractures. J Bone Jt Surg JBJS 96(9):e72. doi:10.2106/JBJS.L.01220
Fripp J, Crozier S, Warfield SK, Ourselin S (2007) Automatic segmentation of the bone and extraction of the bone–cartilage interface from magnetic resonance images of the knee. Phys Med Biol 52(6):1617. http://stacks.iop.org/0031-9155/52/i=6/a=005
Görres J, Brehler M, Franke J, Barth K, Vetter SY, Córdova A, Grützner PA, Meinzer HP, Wolf I, Nabers D (2014) Intraoperative detection and localization of cylindrical implants in cone-beam ct image data. Int J Comput Assist Radiol Surg 9:1–13
Kalinosky B, Sabol JM, Piacsek K, Heckel B, Schmidt TG (2011) Quantifying the tibiofemoral joint space using x-ray tomosynthesis. Med Phys 38(12):6672–6682
Mehling I, Rittstieg P, Mehling A, Küchle R, Müller L, Rommens P (2013) Intraoperative c-arm ct imaging in angular stable plate osteosynthesis of distal radius fractures. J Hand Surg Eur Vol 38(7):751–757
Nolden M, Zelzer S, Seitel A, Wald D, Müller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I (2013) The medical imaging interaction toolkit: challenges and advances. Int J Comput Assist Radiol Surg 8(4):607–620. doi:10.1007/s11548-013-0840-8
Rockwood C, Bucholz R, Green D, Court-Brown C, Heckman J, Tornetta P (2010) Rockwood and Green’s fractures in adults. No. Bd. 1 in Fractures in Adults. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia
Saxena A (2012) International advances in foot and ankle surgery. springer, New York
Schafer S, Nithiananthan S, Mirota D, Uneri A, Stayman J, Zbijewski W, Schmidgunst C, Kleinszig G, Khanna A, Siewerdsen J (2011) mobile C-arm cone-beam CT for guidance of spine surgery: image quality, radiation dose, and integration with interventional guidance. Med Phys 38(8):4563–4574
Valette S, Chassery JM (2004) Approximated centroidal voronoi diagrams for uniform polygonal mesh coarsening. In: Computer graphics forum, vol. 23, pp 381–389. Wiley Online Library
Valette S, Chassery JM, Prost R (2008) Generic remeshing of 3d triangular meshes with metric-dependent discrete Voronoi diagrams. Vis Comput Graph IEEE Trans 14(2):369–381
Von Recum J, Wendl K, Vock B, Grützner PA, Franke J (2012) Intraoperative 3d c-arm imaging: state of the art. Der Unfallchirurg 115(3):196–201. doi:10.1007/s00113-011-2119-2
Wirth S, Euler E, Linsenmaier U, Heining SM, Kotsianos D, Pfeifer KJ, Mutschler W, Reiser M (2004) C-arm-based mobile computed tomography: a comparison with established imaging on the basis of simulated treatments of talus neck fractures in a cadaveric study. Comput Aided Surg 9(1–2):27–38
Yang Z, Fripp J, Chandra SS, Neubert A, Xia Y, Strudwick M, Paproki A, Engstrom C, Crozier S (2015) Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images. Phys Med Biol 60(4):1441
Yin Y, Zhang X, Williams R, Wu X, Anderson DD, Sonka M (2010) Logismos—layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint. Med Imaging IEEE Trans 29(12):2023–2037
Zheng Y, John M, Liao R, Boese J, Kirschstein U, Georgescu B, Zhou SK, Kempfert J, Walther T, Brockmann G, Comaniciu D (2010) Automatic aorta segmentation and valve landmark detection in c-arm ct: application to aortic valve implantation. In: Medical image computing and computer-assisted intervention—MICCAI 2010, pp 476–483. Springer
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This work was partially funded by Siemens Healthcare, X-ray Products.
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Michael Brehler, Joseph Görres, Sven Y. Vetter, Jochen Franke, Paul A. Grützner, Hans-Peter Meinzer and Ivo Wolf declare that they have no conflict of interest.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Brehler, M., Görres, J., Vetter, S.Y. et al. Intra-operative assessment of fractured articular surfaces in cone beam CT image data. Int J CARS 11, 603–612 (2016). https://doi.org/10.1007/s11548-015-1304-0
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DOI: https://doi.org/10.1007/s11548-015-1304-0